R/NormalizationMethods.R

In Linlab-slu/TSSr: TSS sequencing data analysis

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#' Normalize raw TSS counts
#'
#' @description Normalizes raw TSS counts in all samples by tags per million (TPM)
#'
#' @usage normalizeTSS(object)
#'
#' @param object A TSSr object.
#'
#' @export
#'
#' @examples
#' data(exampleTSSr)
#' normalizeTSS(exampleTSSr)
setGeneric("normalizeTSS",function(object)standardGeneric("normalizeTSS"))
#' @rdname normalizeTSS
#' @return Large List of elements - one element for each sample
#' @export
setMethod("normalizeTSS",signature(object = "TSSr"), function(object){
  message("\nNormalizing TSS matrix...")
  ##initialize values
  Genome <- .getGenome(object@genomeName)
  sampleLabelsMerged <- object@sampleLabelsMerged
  objName <- deparse(substitute(object))

  tss.dt <- object@TSSprocessedMatrix
  is.wholenumber <- function(x, tol = .Machine$double.eps^0.5)  abs(x - round(x)) < tol
  if (all(is.wholenumber(object@librarySizes)) == FALSE) {
    stop('\tStopping... data is already normalized')
  }

  library.size <- object@librarySizes
  # if library size is empty, get library size
  # calculate size of genome
  # genomeSize <- 0
  # for (chrom in seq(Genome)) {
  #  genomeSize <- genomeSize + length(Genome[[chrom]])
  # }
  ##normalize tss data
  tss.new <- lapply(as.list(seq(sampleLabelsMerged)), function(i){
    temp <- tss.dt[,.SD, .SDcols = sampleLabelsMerged[i]]
    sizePerMillion <- library.size[i] / 1e6
    setnames(temp, colnames(temp)[[1]], "tags")
    temp[, tags := round(tags / sizePerMillion, 6)]
    setnames(temp, colnames(temp)[[1]], sampleLabelsMerged[i])
    return(temp)
  })
  re <- NULL
  for(i in seq(sampleLabelsMerged)){re <- cbind(re, tss.new[[i]])}
  re <- cbind(tss.dt[,c(1,2,3)],re)
  setorder(re, "strand","chr","pos")
  object@TSSprocessedMatrix <- re
  assign(objName, object, envir = parent.frame())
})